Medical Image Processing Apparatus for Lung Movement Visualization
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Solution Overview
Problem
Current medical image processing techniques cannot effectively visualize and identify differences in movement information within a target site, such as the lungs, which hinders the detection of abnormal movements and lesions.
Innovation Solution
A medical image processing apparatus and method that acquires multiple images over time phases, extracts a target site, sets reference points on the target site and its periphery, and calculates movement information to display relative slippage, allowing for visualization of abnormal movement patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If movement information is calculated for the entire target site, then overall movement can be quantified, but spatial differentiation of movement abnormalities cannot be visualized
Solution Approach 1:
The patent divides the target site into multiple regions of interest (ROIs) along its outline, calculating movement information separately for each region. This segmentation enables both precise movement measurement and spatial differentiation, allowing visualization of movement abnormalities at specific locations within the target site.
Solution Approach 2:
The patent applies different processing to different regions of the target site by calculating movement information for each ROI individually. This local quality approach allows identification of specific areas with abnormal movement patterns, such as adhesion regions in the lung, while maintaining overall movement context.
2Ease of operation
If reference points are set only at the periphery of the target site, then peripheral movement can be measured, but internal position differences cannot be visualized
Solution Approach 1:
The patent sets reference points at multiple locations along the outline of the target site rather than at a single peripheral location. This segmentation of reference points enables measurement of movement at different positions, including internal regions, while maintaining operational simplicity through automated reference point placement.
Solution Approach 2:
The patent transitions from measuring movement at a single peripheral point to measuring movement across multiple points distributed along the outline, adding spatial dimensionality to the measurement. This enables visualization of movement differences at various positions within and around the target site.
3Productivity
If movement information is not associated with specific positions, then overall movement can be analyzed, but abnormal positions cannot be identified
Solution Approach 1:
The patent segments the target site into multiple ROIs along the outline and calculates movement information for each region separately. This segmentation associates movement data with specific positions, enabling efficient analysis while facilitating detection of abnormal positions through visualization of regional movement differences.
Solution Approach 2:
The patent visualizes movement information by assigning different colors or intensity levels to different ROIs based on their movement characteristics. This color-coding system enables rapid identification of abnormal positions through visual inspection, improving detection efficiency without sacrificing analysis productivity.
Data Source
AI summary
From a plurality of medical images in time phases, a target site is extracted from at least one medical image, a reference point is set on each of a target-site side, and a periphery side of the target site which are on across from each other over an outline of the extracted target site, and movement information for the reference points is calculated.